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Noise Models, Denoising Filters and Applications

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Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 4 June 2020 A comparative analysis of image denoising problem: noise models, denoising filters and applications Subrato Bharati1, Tanvir Zaman Khan2, Prajoy Podder1* , Nguyen Quoc Hung3 1 Institute of ICT, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. 2Department of Information & Communication Engineering (ICE), Noakhali Science and Technology University (NSTU), Noakhali-3814, Bangladesh. 3Department of Information Systems, School of Business Information Technology, University of Economics Ho Chi Minh city, 70000, VN, Viet Nam Abstract. Noise reduction in medical images is a perplexing undertaking for the researchers in digital image processing. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as gathering of data and existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Medical images are debased through noise through its transmission and procurement. Image with noise reduce the image contrast and resolution, thereby decreasing the diagnostic values of the medical image. This paper mainly focuses on Gaussian noise, Pepper noise, Uniform noise, Salt and Speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as reorganization of images. Here four types of noises have been undertaken and applied on medical images. Besides numerous filtering methods like Gaussian, median, mean and Weiner applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), Average difference value (AD) and Maximum difference value (MD) to diminish the noises without corrupting the medical image data. Keywords: Gaussian noise, Speckle Noise, Mean square error(MSE), DE noising filters, Maximum difference value (MD), Peak signal to noise ratio(PSNR) 1 Introduction Noise illustrate undesirable data, which break down the picture quality. It is characterized as a procedure which influences the obtained picture. Noise is introduced into pictures ordinarily while exchanging and gaining them. An Image noise causes irregular variety of contrasts in the images. Images are influenced by different sorts of commotion, for example, Gaussian noise generate via regular sources for example particles thermal vibration as well as separate nature of radiation of warm object [1], Exponential noise, Speckle noise is an intricate phenomenon, which corrupts picture value with back scattered wave presence which begins from numerous microscopic dispersed reflections that going over interior organs besides © 2020 by the author(s). Distributed under a Creative Commons CC BY license. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 4 June 2020 creates it more troublesome for the spectator to distinguish fine element of the image in investigative checkups [2], Salt-and-Pepper noise [3] likewise alluded as information drop noise. The picture is not completely ruined by salt & pepper noise rather than some pixel qualities are altered through noise. In spite of noisy image, there is a plausibility of some neighbor’s un-change. In case of data transmission this noise is found. Image pixel qualities are supplanted by defiled values of pixel either greatest "or" least pixel value specifically, 0 "otherwise" 255 individually. if transmission bits are 8. Poisson noise [4] and Periodic noise is produced from gadgets obstructions, particularly in power signal during image procurement. This noise has uncommon qualities corresponding spatially reliant besides sinusoidal in environment at products of a particular frequency. Periodic noise can be advantageously evacuated by utilizing a notch filter or narrow band reject filter [5], Uniform noise presence is essential in amplitude quantization procedure. It introduces, because of analog data changed over into digital data. In this noise model, the signal to noise ratio (SNR) is restricted by the least and highest pixel esteem [6]. The principle sort of noise which is happening during the picture procurement is called Gaussian noise. Then again salt and pepper noise is for the most part presented while transmitting image information over an unsecure correspondence filter. Along these lines, it is required to dispose of the noise from the picture to get exact information and degenerate the visual nature of the pictures. There are different kind of filters use for picture noise decrease. For example, Geometric mean filter, Harmonic mean filter, Median filter, Weiner filter, Midpoint filter, Max & min filter, Alpha-trimmed mean filter, Adaptive filter, Band pass filter, Notch filter, Band reject filter and so on. With a specific end goal to expel noise and enhance visual quality in this structure four dissimilar filter are utilized. Besides filter output data are evaluated utilizing five filters superiority measures parameters. Histograms of these loud pictures are additionally discovered. The state of the histogram of the images has been demonstrated the kind of the noise present in the images. Fig.1 demonstrates the image degradation as well as restoration process where the input image is f(x,y) and g(x,y)is the noisy image. Image obtained after the restoration block is called the filtered image [7]. The paper is oriented in this fashion; related works are described in section II. The outline of the working process is described in section III. Types of image noise describes in division IV, under this division Gaussian, speckle, salt and pepper and uniform noises are illustrated. Division V designates the de noising filters like mean, Gaussian, median and Weiner filters. Besides experimental results represent in division VI and division VII accomplishes this paper. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 4 June 2020 Fig. 1. Image degradation and restoration model 2 Related Works Several researchers already worked on reducing the image noise. In the field of medical imaging systems, de-noising is an important image processing tasks. If noise can be removed automatically from a noisy medical image then it will improve the quality of diagnosis. Therefore doctor gives necessary treatment observing that image. Thanh et. al. [19] provided a review of the Poisson removal methods such as modified ROF model, bilateral filter PURE-LET method etc. in his paper as CT and X-ray image de-noising methods. Computed tomography (CT) and X-Ray imaging systems use the X radiation for capturing the images. So, they are usually corrupted by Poisson noise. Erkan et. al. [20] proposed an Iterative Mean (IM) Filter for eliminating the salt-and-pepper noise. The mean of gray values of noise-free pixels are used in IMF in a fixed-size window. Thanh et. al. [21] proposed an Adaptive Frequency Median Filter in order to remove the salt and pepper noise. The proposed filter in employs frequency median for restoring the grey values of the corrupted pixels. Thanh et. al. also proposed and implemented adaptive TV-L1 regularization model for eliminating the salt and pepper noise [22]. Inspiring the above research works, we have implemented several type of filters to remove various types of noise. 3 Overview of the working process Figure 2 summarizes the working procedures Gaussian, median, mean and Weiner filter is used in this working diagram for eradicating the undesired noise from the image. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 4 June 2020 Fig. 2. Flowchart of working procedure 4 Types of Image Noise 4.1 Gaussian Noise Gaussian noise is likewise called enhancer noise or random variation impulsive noise. Gaussian noise is created because of (an) electronic circuit noise, (b) sensor noise because of high temperature, (c) sensor noise because of poor brightening [8][9]. It is a sort of measurable noise where the sufficiency of the noise takes after Gaussian dissemination [10]. Gaussian noise emerges as probability density function of the regular distribution. The PDF of Gaussian noise, (1) Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 4 June 2020 4.2 Speckle Noise In [9], [10] Speckle noise is one kind of granular noise and the picture quality has been degraded by this speckle noise. The images which are acquired from medical are spoiled by the speckle noise. Generally, speckle noise expands the mean gray near of a native area and causing difficulties in medical because of coherent processing of backscattered signal [23]. 4.3 Uniform Noise Uniform or Quantization noise is created by quantizing the pixels of a detected picture to various discrete levels. In spite of the fact that it can be flagged subordinate however it will be flagged free if the sources of other noise are sufficiently enormous to source dithering [8]. The PDF of uniform noise is given by, (2) Mean value is specified by, (3) Variance is given by, (4) 4.4 Salt and Pepper Noise Salt & Pepper noise is called as Impulse noise. It can likewise be named as spike or fat–tailed distributed noise [11]. The salt and pepper noise is brought about by sudden and sharp disorder in the image signal. It is appearance as arbitrarily scattered dark or white (or together) pixels above the image. It is digitized as great qualities in the image. Impulse noise contained image has dark pixels in the bright region and splendid pixels in dull areas [8]. The PDF of (bipolar) impulse noise can be communicated as [8]. (5) If b>a, gray level b will perform as a light dot in the image. On the contrary, level a will act similar to a dark dot. The impulse noise is called uni-polar when Pa or Pb is zero. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 4 June 2020 5 Image De-Noise Filters Image de-noising filters have an extensive variety of uses. For example, medical image investigation, signal (video, sound, voice) examination, information removal, radio space science, etc.
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